Perfusion CT (PCT) is a technique for assessing information pertaining to the passage of fluid (e.g., blood, lymph, etc.) through anatomical tissue to facilitate identifying a health state of the tissue or other tissue. It involves acquiring sequential images of an anatomical location after the injection of contrast material and allows obtaining vital functional physiology information. However, the anatomical coverage of this technique is per se relatively small. In order to increase the anatomical coverage and at the same time avoid requiring more scan time and a larger amount of contrast material in multiple PCT scans, it is known to operate a CT imaging apparatus in a jog mode. In the jog mode, a scanner table of the CT imaging apparatus is moved back and forth between different neighboring but non-overlapping imaging positions within a single study. Due to breathing motion or other movement of a person or an object (organ, tissue, etc.) under study, gaps or overlaps between different sets of CT imaging data taken at different imaging positions can occur. Thus, the CT imaging data acquired at different times and different imaging positions may not relate well since the size of a gap or overlap between the different sets of CT imaging data acquired at different times and different imaging positions is not known. The original advantage of the jog mode, i.e., a larger anatomical coverage may therefore not be fully achieved.
The publication N. K. G. Jensen et al., “Prediction and Reduction of Motion Artifacts in Free-Breathing Dynamic Contrast Enhanced CT Perfusion Imaging of Primary and Metastatic Intrahepatic Tumors”, Academic Radiology, Vol. 20, No. 4, April 2013, pages 414 to 422, describes a method for predicting and reducing motion artifacts in free-breathing liver perfusion computed tomography (CT) scanning with couch shuttling and to compare tumor and liver parenchyma perfusion values. A semiautomatic respiratory motion correction algorithm is applied to align the acquired images along the z-axis. Perfusion maps are generated using the dual-input Johnson-Wilson model. Root mean squared deviation (RMSD) maps of the model fit to the pixel time-density curves are calculated.
U.S. Pat. No. 9,002,089 B2 describes a method of registering a 4D contrast enhanced image data set. The four-dimensional (4D) contrast enhanced image data set covering three spatial dimensions and the time dimension includes image data of the same volume of interest acquired at different time frames with changing contrast enhancement, the volume of interest includes moving structure, and the different time frames correspond to a predetermined motion phase of interest in different motion cycles of the moving structure. The method comprises: registering image data corresponding to a plurality of different timeframes with reference image data from one of the timeframes. The image data and the reference image data correspond to a same volume of interest and different time frames.